Summary of Generalized Cauchy-schwarz Divergence and Its Deep Learning Applications, by Mingfei Lu et al.
Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications
by Mingfei Lu, Chenxu Li, Shujian Yu, Robert Jenssen, Badong Chen
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new divergence measure, called the generalized Cauchy-Schwarz divergence (GCSD), designed specifically for quantifying the total divergence among multiple distributions. This is crucial in applications such as clustering, multi-source domain adaptation, and multi-view learning. The GCSD is accompanied by a kernel-based closed-form sample estimator, making it efficient and easy to use in machine-learning tasks. The authors demonstrate the effectiveness of GCSD in deep clustering and multi-source domain adaptation experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to measure how different multiple groups of things are from each other. This is important when we need to compare many groups at once, like grouping similar objects or matching data from different sources. The new method, called GCSD, is designed specifically for this task and is easy to use in machine learning applications. The authors tested GCSD on two tasks: organizing similar objects together and adapting to new data sources. Their results show that GCSD works well and can improve how we do these tasks. |
Keywords
» Artificial intelligence » Clustering » Domain adaptation » Machine learning
